Most Recent NVIDIA NCA-GENL Exam Dumps

Limited throughput between CPU and GPU often results from data transfer bottlenecks or inefficient resource utilization. NVIDIA's documentation on optimizing deep learning workflows (e.g., using CUDA and cuDNN) suggests the following:

Option B: Memory pooling techniques, such as pinned memory or unified memory, reduce data transfer overhead by optimizing how data is staged between CPU and GPU.

Option C: Upgrading to a higher-end GPU (e.g., NVIDIA A100 or H100) increases computational capacity and memory bandwidth, improving throughput for data-intensive tasks.

Option A (increasing CPU clock speed) has limited impact on CPU-GPU data transfer bottlenecks, and Option D (increasing CPU cores) is less effective unless the workload is CPU-bound, which is uncommon in GPU-accelerated deep learning.

NVIDIA CUDA Documentation: https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html

NVIDIA GPU Product Documentation: https://www.nvidia.com/en-us/data-center/products/

Zero-shot learning allows models to perform tasks or classify data into categories without prior training on those specific categories. In NLP, pre-trained language models (e.g., BERT, GPT) with semantic embeddings are highly effective for zero-shot learning because they encode general linguistic knowledge and can generalize to new tasks by leveraging semantic similarity. NVIDIA's NeMo documentation on NLP tasks explains that pre-trained LLMs can perform zero-shot classification by using prompts or embeddings to map input text to unseen categories, often via techniques like natural language inference or cosine similarity in embedding space. Option A (rule-based systems) lacks scalability and flexibility. Option B contradicts zero-shot learning, as it requires labeled data. Option C (training from scratch) is impractical and defeats the purpose of zero-shot learning.

NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html

Brown, T., et al. (2020). 'Language Models are Few-Shot Learners.'

Cosine similarity is the most commonly used metric to measure the semantic closeness of two text passages in NLP. It calculates the cosine of the angle between two vectors (e.g., word embeddings or sentence embeddings) in a high-dimensional space, focusing on the direction rather than magnitude, which makes it robust for comparing semantic similarity. NVIDIA's documentation on NLP tasks, particularly in NeMo and embedding models, highlights cosine similarity as the standard metric for tasks like semantic search or text similarity, often using embeddings from models like BERT or Sentence-BERT. Option A (Hamming distance) is for binary data, not text embeddings. Option B (Jaccard similarity) is for set-based comparisons, not semantic content. Option D (Euclidean distance) is less common for text due to its sensitivity to vector magnitude.

NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html

Attention mechanisms in transformer models, as introduced in 'Attention is All You Need' (Vaswani et al., 2017), allow the model to focus on relevant parts of the input sequence by assigning higher weights to important tokens during processing. NVIDIA's NeMo documentation explains that self-attention enables transformers to capture long-range dependencies and contextual relationships, making them effective for tasks like language modeling and translation. Option B is incorrect, as attention does not compress sequences but processes them fully. Option C is false, as attention is not about generating noise. Option D refers to embeddings, not attention.

Vaswani, A., et al. (2017). 'Attention is All You Need.'

NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html

Chain-of-thought (CoT) prompting is a highly effective technique for improving large language model (LLM) performance on multi-step reasoning tasks. By including explicit intermediate steps in the prompt, CoT guides the model to break down complex problems into manageable parts, improving reasoning accuracy. NVIDIA's NeMo documentation on prompt engineering highlights CoT as a powerful method for tasks like mathematical reasoning or logical problem-solving, as it leverages the model's ability to follow structured reasoning paths. Option A is incorrect, as retrieval-augmented generation (RAG) without context is less effective for reasoning tasks. Option B is wrong, as unrelated examples in few-shot prompting do not aid reasoning. Option C (zero-shot prompting) is less effective than CoT for complex reasoning.

NVIDIA NeMo Documentation: https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/nlp/intro.html

Wei, J., et al. (2022). 'Chain-of-Thought Prompting Elicits Reasoning in Large Language Models.'